弱GPS环境下的智能车辆自主导航算法  

Autonomous navigation algorithm for intelligent vehicle in weak GPS environment

作　　者：袁朝春[1] 朱昊宇 何友国[1] JIE Shen 陈龙[1] YUAN Chaochun;ZHU Haoyu;HE Youguo;JIE Shen;CHEN Long(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang,Jiangsu 212013,China;Department of Computer and Information Science,University of Michigan-Dearborn,Dearborn,Michigan MI 48128,USA)

机构地区：[1]江苏大学汽车工程研究院,江苏镇江212013 [2]密西根大学迪尔本分校计算机科学与信息科学系,密西根州迪尔本MI 48128

出　　处：《江苏大学学报（自然科学版）》2023年第1期22-28,共7页Journal of Jiangsu University：Natural Science Edition

基　　金：国家自然科学基金资助项目(51775247)。

摘　　要：针对城市环境GPS(global positioning system)易定位失效的问题,提出一种利用视觉位置识别的智能汽车自主导航算法.为了识别车辆在无GPS环境中的全局位置,通过注意力模型和细粒度特征提取模块得到图像中具有判别力的视觉特征,实现航拍图像与离线卫星图像的匹配检索.在获取车辆位置信息基础上,通过精英蚁群优化算法为车辆输出前方道路分支行驶的方向,进行全局路径规划.结果表明:细粒度特征提取模块提取更具判别力特征,利用标签平滑的交叉熵损失函数训练,实现了实际环境位置的有效识别,车辆可以利用所提算法在弱GPS环境中自主导航.To solve the problem that global positioning system(GPS) was easy to fail in urban environment, an autonomous navigation algorithm for intelligent vehicle was proposed by visual location recognition. To identify the global position of vehicle in the GPS-free environment, the discriminative visual features in the image were obtained through the attention model and the fine-grained feature extraction module, and the matching retrieval between the aerial image and the offline satellite image was realized. According to the vehicle position information, the elite ant colony optimization algorithm was used to output the direction of the road branch ahead for the vehicle and perform global path planning. The results show that the fine-grained feature extraction module can extract more discriminative features. The label-smoothed cross-entropy loss function training can be used to achieve effective identification of actual environmental locations, and vehicles can use the proposed algorithm to navigate autonomously in weak GPS environment.

关 键 词：智能车 视觉位置识别 路径规划 注意力模型 精英蚁群算法 

分 类 号：U463.6[机械工程—车辆工程] TP391.41[交通运输工程—载运工具运用工程]